Some time ago, we published our #biopixR package in #JOSS
https://social.anoxinon.de/@RoedigerRG/113340746869808933
Now Tim @devTLB and I are working on our review as promised here.
https://social.anoxinon.de/@RoedigerRG/112773962698337008
The good news is, we have done most of the work on it. Now we are just polishing the paper.
We will likely share a link to a preprint server. Let's see how things evolve.
Today Tim gave a quite vibrant presentation of the #biopixR package at our faculty at the #BTUCS. It was great to hear that some colleagues find it useful and want to use it for their research.
News!
biopixR, has reached version 1.0.0! From its humble beginnings to the latest updates, we've worked tirelessly to bring you powerful tools for image analysis and processing.
Improved examples
New functions: importImage and scanDir
Automated parameter calculation in objectDetection
Faster computation through vectorization
Browse through our journey: from edge detection to automated calculations, parallel processing, and more!
Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) <<a href="https://doi.org/10.21037%2Fjlpm.2019.04.05" target="_top">doi:10.21037/jlpm.2019.04.05</a>>).
ο»Ώπ’ News! A new version update of #biopixR is now available π
π biopixR - Package for analysis of bioimage image data: Make your bioimaging workflow easier and more efficient with this tool.
π Just a few days ago, biopixR made its first public appearance on CRAN! Check it out here: <https://cran.r-project.org/package=biopixR>
π€ We've already received valuable feedback from the #rdatatable community with a pull request. Thanks! π
Let's keep this project growing and evolving together! π #BioImaging #rstats
Combines the 'magick' and 'imager' packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially microparticles. By providing high throughput pipelines and clustering capabilities, 'biopixR' facilitates efficient insight generation for researchers (Schneider J. et al. (2019) <<a href="https://doi.org/10.21037%2Fjlpm.2019.04.05" target="_top">doi:10.21037/jlpm.2019.04.05</a>>).
Tim released #biopixR 0.2.2. π He introduced imgPipe, a function that integrates multiple filter functions sequentially for efficient image data analysis. Users can selectively apply filters based on their specific requirements, and also analyze images using multiple color channels for more detailed exploration. The system includes a time stamp log to maintain traceability and reproducibility by recording all the steps taken during the analysis process.
In my working group, we work on an open source program for #rstats, with which you can process image data.
In addition to its algorithms, #biopixR is also based on the functionality of #imager and #magick. With the package, you can analyze microparticles in droplets, among other things.
The main developer of the program is Tim. π¨βπ» He does his bachelor's thesis on it. π¨βπ
Today, he has published the first stable version 0.1.0 on GitHub.
π
https://github.com/Brauckhoff/biopixR
RoedigerRG
devSJR 
